Tehnički vjesnik, Vol. 28 No. 2, 2021.
Izvorni znanstveni članak
https://doi.org/10.17559/TV-20210121100916
An OLS and GMM Combined Algorithm for Text Analysis for Heterogeneous Impact in Online Health Communities
Yunqiu Zhang
; School of Economics and Management, Beijing Jiaotong University, No. 3,Shangyuancun, Haidian District, Beijing, China
Jack Strauss
; Reiman School of Finance, University of Denver, 2101 S. University Blvd., Denver, USA
Hongchang Li*
; School of Economics and Management, Beijing Jiaotong University, No. 3,Shangyuancun, Haidian District, Beijing, China
Lihong Liu
; Department of Economics, Party School of the Beijing Municipal Committee, No. 6, Chegongzhuang Street, Xicheng District, Beijing, China
Sažetak
The increase of doctors' activity in online health communities (OHCs) plays a decisive role in their development. Although the literature on the determinants of doctors' online activities has received considerable attention, the impact of illness severity on these factors remains rare. A network externality analytical framework is constructed to explain the factors (that is, responsiveness, involvement, word-of-mouth, incentives, price, titles and gender) affecting online doctors' behavior, and assess whether factors differ by. By developing text analysis of 4916 doctors' data from a Chinese OHC, this paper applies ordinary least squares (OLS) and General Method of Moments (GMM) to analyze whether the determinants are equal across serious, moderate, and mild illnesses. Our experiment results find that the determinants affecting doctors' online service activity substantially differ across illness severity. Experiments prove the effectiveness of the proposed OLS and GMM methods and demonstrate that they are applicable in online medical field.
Ključne riječi
doctor activity; GMM; illness severity; network externalities; OLS; online health communities
Hrčak ID:
255829
URI
Datum izdavanja:
17.4.2021.
Posjeta: 1.325 *